Customer-side electricity load management for sustainable manufacturing systems utilizing combined heat and power generation system

Customer-side electricity load management can effectively improve the reliability of electricity grid and reduce the cost of electricity for customers. Combined heat and power (CHP) generation systems have been considered a promising method to implement electricity load management and have been widely applied in commercial and residential building sectors. Recently, the benefit of CHP application in electricity load management in industrial sector has also been gradually recognized. On-site generated electricity by a CHP system can be utilized to support the operation of industrial equipment and thus the cost of electricity purchased from the grid can be reduced. In this paper, we focus on the utilization of CHP in electricity load management for industrial manufacturing systems to examine the benefits regarding cost savings for the manufacturers. The optimal schedule for both the manufacturing and the CHP systems under a Time-of-Use (TOU) electricity tariff can be identified by minimizing the electricity billing cost and CHP operation cost under the constraint of production throughput. Mixed-Integer Nonlinear Programming (MINLP) formulation is developed to model this scheduling problem mathematically. Particle Swarm Optimization (PSO) is used to find a near optimal solution for the problem with a reasonable computational cost. A numerical case study is used to illustrate the effectiveness of the proposed method.

[1]  Y. Wang,et al.  Time-of-use based electricity demand response for sustainable manufacturing systems , 2013 .

[2]  Zeyi Sun,et al.  Inventory control for peak electricity demand reduction of manufacturing systems considering the tradeoff between production loss and energy savings , 2014 .

[3]  Z. Vale,et al.  Demand response in electrical energy supply: An optimal real time pricing approach , 2011 .

[4]  Yong Wang,et al.  Time-of-use based electricity cost of manufacturing systems: Modeling and monotonicity analysis , 2014 .

[5]  Yu Ding,et al.  Season-Dependent Condition-Based Maintenance for a Wind Turbine Using a Partially Observed Markov Decision Process , 2010, IEEE Transactions on Power Systems.

[6]  Nelson Fumo,et al.  Benefits of thermal energy storage option combined with CHP system for different commercial building types , 2013 .

[7]  Bart De Schutter,et al.  Demand Response With Micro-CHP Systems , 2011, Proceedings of the IEEE.

[8]  Fuh-Der Chou,et al.  PARTICLE SWARM OPTIMIZATION WITH COCKTAIL DECODING METHOD FOR HYBRID FLOW SHOP SCHEDULING PROBLEMS WITH MULTIPROCESSOR TASKS , 2013 .

[9]  Henrik Madsen,et al.  A model predictive control strategy for the space heating of a smart building including cogeneration of a fuel cell-electrolyzer system , 2014 .

[10]  Lingfeng Wang,et al.  Intelligent Multiagent Control System for Energy and Comfort Management in Smart and Sustainable Buildings , 2012, IEEE Transactions on Smart Grid.

[11]  M. Burgos Payán,et al.  Optimum design of transmissions systems for offshore wind farms including decision making under risk , 2013 .

[12]  Michael A. Gerber,et al.  EnergyPlus Energy Simulation Software , 2014 .

[13]  M. P. Moghaddam,et al.  Optimal real time pricing in an agent-based retail market using a comprehensive demand response model , 2011 .

[14]  Marija Mandic,et al.  Possibilities of implementation of CHP (combined heat and power) in the wood industry in Serbia , 2012 .

[15]  G. Moslehi,et al.  A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search , 2011 .

[16]  Kurt Roth,et al.  Using CHP Systems In Commercial Buildings , 2005 .

[17]  Andrea Toffolo,et al.  Improving energy efficiency of sawmill industrial sites by integration with pellet and CHP plants , 2013 .

[18]  Pisut Pongchairerks Particle swarm optimization algorithm applied to scheduling problems , 2009 .

[19]  Fehmi Tanrisever,et al.  Forecasting electricity infeed for distribution system networks: An analysis of the Dutch case , 2013 .

[20]  Changsun Ahn,et al.  Optimal decentralized charging control algorithm for electrified vehicles connected to smart grid , 2011 .

[21]  A. Alessandri,et al.  Electricity demand forecasting over Italy: Potential benefits using numerical weather prediction models , 2013 .

[22]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[23]  Kornelis Blok,et al.  CO2 emission reduction by means of industrial CHP in the Netherlands , 1994 .